Path Planning of UAV by Combing Improved Ant Colony System and Dynamic Window Algorithm  

作　　者：徐海芹 邢浩翔 刘洋 XU Haiqin;XING Haoxiang;LIU Yang(College of Information Sciences and Technology,Donghua University,Shanghai 201620,China)

机构地区：[1]College of Information Sciences and Technology,Donghua University,Shanghai 201620,China

出　　处：《Journal of Donghua University(English Edition)》2023年第6期676-683,共8页东华大学学报（英文版）

基　　金：National Natural Science Foundation of China(No.62241503);Natural Science Foundation of Shanghai,China(No.22ZR1401400)。

摘　　要：A fusion algorithm is proposed to enhance the search speed of an ant colony system(ACS)for the global path planning and overcome the challenges of the local path planning in an unmanned aerial vehicle(UAV).The ACS search efficiency is enhanced by adopting a 16-direction 24-neighborhood search way,a safety grid search way,and an elite hybrid strategy to accelerate global convergence.Quadratic planning is performed using the moving average(MA)method.The fusion algorithm incorporates a dynamic window approach(DWA)to deal with the local path planning,sets a retracement mechanism,and adjusts the evaluation function accordingly.Experimental results in two environments demonstrate that the improved ant colony system(IACS)achieves superior planning efficiency.Additionally,the optimized dynamic window approach(ODWA)demonstrates its ability to handle multiple dynamic situations.Overall,the fusion optimization algorithm can accomplish the mixed path planning effectively.

关 键 词：ant colony system(ACS) dynamic window approach(DWA) path planning dynamic obstacle 

分 类 号：V279[航空宇航科学与技术—飞行器设计]